code: def selection_r(
      acquisition_function,
      samples_y_aggregation,
      x_bounds,
      x_types,
      regressor_gp,
      num_starting_points=100,
      minimize_constraints_fun=None,
  ):
      """
      Selecte R value
      """
      minimize_starting_points = [
          lib_data.rand(x_bounds, x_types)
          for i in range(0, num_starting_points)
      ]
      outputs = selection(
          acquisition_function,
          samples_y_aggregation,
          x_bounds,
          x_types,
          regressor_gp,
          minimize_starting_points,
          minimize_constraints_fun=minimize_constraints_fun,
      )
  
      return outputs
  
file_path: nni/nni/algorithms/hpo/metis_tuner/Regression_GP/Selection.py
imports: import os
  import random
  import sys
  from ..None import lib_acquisition_function
  from ..None import lib_constraint_summation
  from ..None import lib_data
  from .None import Prediction
label_desc: Use image node_id 1 for calling a global function with example usage: selection_r(acquisition_function, samples_y_aggregation, x_bounds, x_types, regressor_gp, num_starting_points, minimize_constraints_fun) and returns: outputs